A Boltzmann-Based Estimation of Distribution Algorithm for a General Resource Scheduling Model


Most researchers employed common purposeful models when managing scheduling issues with controllable processing times. However, in many difficult producing systems with a high diversity of jobs, these practical resource models fail to reflect their specific characteristics. To fulfill these requirements, we apply a a lot of general model, the discrete model. Traditional purposeful models will be viewed as special cases of such model. During this paper, the discrete model is implemented on a drawback of minimizing the weighted resource allocation subject to a typical deadline on one machine. By reducing the matter to a partition downside, we demonstrate that it is NP-complete, which addresses the tough issue of the guarantee of each the answer quality and time cost. So as to tackle the problem, we develop an estimation of distribution algorithm primarily based on an approximation of the Boltzmann distribution. The approximation strategy represents a tradeoff between complexity and resolution accuracy. The results of the experiments conducted on benchmarks show that, compared with alternative various approaches, the proposed algorithm has competitive behavior, obtaining 74 best solutions out of 90 instances.

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